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A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems
Abstract : This paper focuses on solving the problems of preparing and normalizing data that are captured from a classroom observation, and are linked with significant relevant properties. We adapt these data using a Bayesian model that creates normalization conditions to a well fitted artificial neural network. We separate the method in two stages: first implementing the data variable in a functional multi-factorial normalization analysis using a normalizing constant and then using constructed vectors containing normalization values in the learning and testing stages of the selected learning vector quantifier neural network.
Cited literature [13 references]
https://hal.inria.fr/hal-01060648
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Submitted on : Friday, November 17, 2017 - 2:21:57 PM
Last modification on : Thursday, March 5, 2020 - 5:43:04 PM
Long-term archiving on: : Sunday, February 18, 2018 - 3:18:03 PM
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, November 17, 2017 - 2:21:57 PM
Last modification on : Thursday, March 5, 2020 - 5:43:04 PM
Long-term archiving on: : Sunday, February 18, 2018 - 3:18:03 PM
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PoulosBA10.pdf
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Distributed under a Creative Commons Attribution 4.0 International License